docs: Add comprehensive Framework-Hooks documentation

Complete technical documentation for the SuperClaude Framework-Hooks system: • Overview documentation explaining pattern-driven intelligence architecture • Individual hook documentation for all 7 lifecycle hooks with performance targets • Complete configuration documentation for all YAML/JSON config files • Pattern system documentation covering minimal/dynamic/learned patterns • Shared modules documentation for all core intelligence components • Integration guide showing SuperClaude framework coordination • Performance guide with optimization strategies and benchmarks Key technical features documented: - 90% context reduction through pattern-driven approach (50KB+ → 5KB) - 10x faster bootstrap performance (500ms+ → <50ms) - 7 lifecycle hooks with specific performance targets (50-200ms) - 5-level compression system with quality preservation ≥95% - Just-in-time capability loading with intelligent caching - Cross-hook learning system for continuous improvement - MCP server coordination for all 6 servers - Integration with 4 behavioral modes and 8-step quality gates Documentation provides complete technical reference for developers, system administrators, and users working with the Framework-Hooks system architecture and implementation. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-29 16:16:08 +00:00 · 2025-08-05 16:50:10 +02:00
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+# Subagent Stop Hook Documentation
+
+## Purpose
+
+The `subagent_stop` hook implements **MODE_Task_Management delegation coordination and analytics** by analyzing subagent task completion performance and providing comprehensive delegation effectiveness measurement. This hook specializes in **task delegation analytics and coordination**, measuring multi-agent collaboration effectiveness and optimizing wave orchestration strategies.
+
+**Core Responsibilities:**
+- Analyze subagent task completion and performance metrics
+- Measure delegation effectiveness and coordination success
+- Learn from parallel execution patterns and cross-agent coordination
+- Optimize wave orchestration strategies for multi-agent operations
+- Coordinate cross-agent knowledge sharing and learning
+- Track task management framework effectiveness across delegated operations
+
+## Execution Context
+
+The `subagent_stop` hook executes **after subagent operations complete** in Claude Code, specifically when:
+
+- **Subagent Task Completion**: When individual subagents finish their delegated tasks
+- **Multi-Agent Coordination End**: After parallel task execution completes
+- **Wave Orchestration Completion**: When wave-based task coordination finishes
+- **Delegation Strategy Assessment**: For analyzing effectiveness of different delegation approaches
+- **Cross-Agent Learning**: When coordination patterns need to be captured for future optimization
+
+**Integration Points:**
+- Integrates with Claude Code's subagent delegation system
+- Coordinates with MODE_Task_Management for delegation analytics
+- Synchronizes with wave orchestration for multi-agent coordination
+- Links with learning engine for continuous delegation improvement
+
+## Performance Target
+
+**Target Execution Time: <150ms**
+
+The hook maintains strict performance requirements to ensure minimal overhead during delegation analytics:
+
+```python
+# Performance configuration
+self.performance_target_ms = config_loader.get_hook_config('subagent_stop', 'performance_target_ms', 150)
+
+# Performance tracking
+execution_time = (time.time() - start_time) * 1000
+coordination_report['performance_metrics'] = {
+    'coordination_analysis_time_ms': execution_time,
+    'target_met': execution_time < self.performance_target_ms,
+    'coordination_efficiency': self._calculate_coordination_efficiency(context, execution_time)
+}
+```
+
+**Performance Optimization Features:**
+- **Fast Context Extraction**: Efficient subagent data parsing and context enrichment
+- **Streamlined Analytics**: Optimized delegation effectiveness calculations
+- **Batched Operations**: Grouped analysis operations for efficiency
+- **Cached Learning**: Reuse of previous coordination patterns for faster analysis
+
+## Delegation Analytics
+
+The hook provides comprehensive **delegation effectiveness measurement** through multiple analytical dimensions:
+
+### Task Completion Analysis
+
+```python
+def _analyze_task_completion(self, context: dict) -> dict:
+    """Analyze task completion performance."""
+    task_analysis = {
+        'completion_success': context.get('task_success', False),
+        'completion_quality': context.get('output_quality', 0.0),
+        'completion_efficiency': context.get('resource_efficiency', 0.0),
+        'completion_time_performance': 0.0,
+        'success_factors': [],
+        'improvement_areas': []
+    }
+```
+
+**Key Metrics:**
+- **Completion Success Rate**: Binary success/failure tracking for delegated tasks
+- **Output Quality Assessment**: Quality scoring (0.0-1.0) based on validation results and error indicators
+- **Resource Efficiency**: Memory, CPU, and time utilization effectiveness measurement
+- **Time Performance**: Actual vs. expected execution time analysis
+- **Success Factor Identification**: Patterns that lead to successful delegation outcomes
+- **Improvement Area Detection**: Areas requiring optimization in future delegations
+
+### Delegation Effectiveness Measurement
+
+```python
+def _analyze_delegation_effectiveness(self, context: dict, task_analysis: dict) -> dict:
+    """Analyze effectiveness of task delegation."""
+    delegation_analysis = {
+        'delegation_strategy': context.get('delegation_strategy', 'unknown'),
+        'delegation_success': context.get('task_success', False),
+        'delegation_efficiency': 0.0,
+        'coordination_overhead': 0.0,
+        'parallel_benefit': 0.0,
+        'delegation_value': 0.0
+    }
+```
+
+**Delegation Strategies Analyzed:**
+- **Files Strategy**: Individual file-based delegation effectiveness
+- **Folders Strategy**: Directory-level delegation performance
+- **Auto Strategy**: Intelligent delegation strategy effectiveness
+- **Custom Strategies**: User-defined delegation pattern analysis
+
+**Effectiveness Dimensions:**
+- **Delegation Efficiency**: Ratio of productive work to coordination overhead
+- **Coordination Overhead**: Time and resource cost of agent coordination
+- **Parallel Benefit**: Actual speedup achieved through parallel execution
+- **Overall Delegation Value**: Composite score weighing quality, efficiency, and parallel benefits
+
+## Wave Orchestration
+
+The hook provides advanced **multi-agent coordination analysis** for wave-based task orchestration:
+
+### Wave Coordination Success
+
+```python
+def _analyze_coordination_patterns(self, context: dict, delegation_analysis: dict) -> dict:
+    """Analyze coordination patterns and effectiveness."""
+    coordination_analysis = {
+        'coordination_strategy': 'unknown',
+        'synchronization_effectiveness': 0.0,
+        'data_flow_efficiency': 0.0,
+        'wave_coordination_success': 0.0,
+        'cross_agent_learning': 0.0,
+        'coordination_patterns_detected': []
+    }
+```
+
+**Wave Orchestration Features:**
+- **Progressive Enhancement**: Iterative improvement through multiple coordination waves
+- **Systematic Analysis**: Comprehensive methodical analysis across wave cycles
+- **Adaptive Coordination**: Dynamic strategy adjustment based on wave performance
+- **Enterprise Orchestration**: Large-scale coordination for complex multi-agent operations
+
+### Wave Performance Metrics
+
+```python
+def _update_wave_orchestration_metrics(self, context: dict, coordination_analysis: dict) -> dict:
+    """Update wave orchestration performance metrics."""
+    wave_metrics = {
+        'wave_performance': 0.0,
+        'orchestration_efficiency': 0.0,
+        'wave_learning_value': 0.0,
+        'next_wave_recommendations': []
+    }
+```
+
+**Wave Strategy Analysis:**
+- **Wave Position Tracking**: Current position within multi-wave coordination
+- **Inter-Wave Communication**: Data flow and synchronization between waves
+- **Wave Success Metrics**: Performance measurement across wave cycles
+- **Orchestration Efficiency**: Resource utilization effectiveness in wave coordination
+
+## Cross-Agent Learning
+
+The hook implements **sophisticated learning mechanisms** for continuous delegation improvement:
+
+### Learning Event Recording
+
+```python
+def _record_coordination_learning(self, context: dict, delegation_analysis: dict, 
+                               optimization_insights: dict):
+    """Record coordination learning for future optimization."""
+    # Record delegation effectiveness
+    self.learning_engine.record_learning_event(
+        LearningType.PERFORMANCE_OPTIMIZATION,
+        AdaptationScope.PROJECT,
+        context,
+        {
+            'delegation_strategy': context.get('delegation_strategy'),
+            'task_type': context.get('task_type'),
+            'delegation_value': delegation_analysis['delegation_value'],
+            'coordination_overhead': delegation_analysis['coordination_overhead'],
+            'parallel_benefit': delegation_analysis['parallel_benefit']
+        },
+        delegation_analysis['delegation_value'],
+        0.8,
+        {'hook': 'subagent_stop', 'coordination_learning': True}
+    )
+```
+
+**Learning Categories:**
+- **Performance Optimization**: Delegation strategy effectiveness patterns
+- **Operation Patterns**: Successful task completion patterns
+- **Coordination Patterns**: Effective multi-agent coordination strategies
+- **Error Recovery**: Learning from delegation failures and recovery strategies
+
+**Learning Scopes:**
+- **Project-Level Learning**: Delegation patterns specific to current project
+- **User-Level Learning**: Cross-project delegation preferences and patterns
+- **System-Level Learning**: Framework-wide coordination optimization patterns
+
+## Parallel Execution Tracking
+
+The hook provides comprehensive **parallel operation performance analysis**:
+
+### Parallel Benefit Calculation
+
+```python
+# Calculate parallel benefit
+parallel_tasks = context.get('parallel_tasks', [])
+if len(parallel_tasks) > 1:
+    # Estimate parallel benefit based on task coordination
+    parallel_efficiency = context.get('parallel_efficiency', 1.0)
+    theoretical_speedup = len(parallel_tasks)
+    actual_speedup = theoretical_speedup * parallel_efficiency
+    delegation_analysis['parallel_benefit'] = actual_speedup / theoretical_speedup
+```
+
+**Parallel Performance Metrics:**
+- **Theoretical vs. Actual Speedup**: Comparison of expected and achieved parallel performance
+- **Parallel Efficiency**: Effectiveness of parallel task coordination
+- **Synchronization Overhead**: Cost of coordinating parallel operations
+- **Resource Contention Analysis**: Impact of resource sharing on parallel performance
+
+### Coordination Pattern Detection
+
+```python
+# Detect coordination patterns
+if delegation_analysis['delegation_value'] > 0.8:
+    coordination_analysis['coordination_patterns_detected'].append('effective_delegation')
+
+if coordination_analysis['synchronization_effectiveness'] > 0.8:
+    coordination_analysis['coordination_patterns_detected'].append('efficient_synchronization')
+
+if coordination_analysis['wave_coordination_success'] > 0.8:
+    coordination_analysis['coordination_patterns_detected'].append('successful_wave_orchestration')
+```
+
+**Pattern Categories:**
+- **Effective Delegation**: High-value delegation strategies
+- **Efficient Synchronization**: Optimal coordination mechanisms
+- **Successful Wave Orchestration**: High-performing wave coordination patterns
+- **Resource Optimization**: Efficient resource utilization patterns
+
+## Configuration
+
+The hook is configured through `superclaude-config.json` with comprehensive settings for delegation analytics:
+
+### Core Configuration
+
+```json
+{
+  "hooks": {
+    "subagent_stop": {
+      "enabled": true,
+      "priority": 7,
+      "performance_target_ms": 150,
+      "delegation_analytics": {
+        "enabled": true,
+        "strategy_analysis": ["files", "folders", "auto"],
+        "effectiveness_threshold": 0.6,
+        "coordination_overhead_threshold": 0.3
+      },
+      "wave_orchestration": {
+        "enabled": true,
+        "wave_strategies": ["progressive", "systematic", "adaptive", "enterprise"],
+        "success_threshold": 0.7,
+        "learning_enabled": true
+      },
+      "parallel_tracking": {
+        "efficiency_threshold": 0.7,
+        "synchronization_tracking": true,
+        "resource_contention_analysis": true
+      },
+      "learning_configuration": {
+        "coordination_learning": true,
+        "pattern_detection": true,
+        "cross_agent_learning": true,
+        "performance_learning": true
+      }
+    }
+  }
+}
+```
+
+### Task Management Configuration
+
+```json
+{
+  "session": {
+    "task_management": {
+      "delegation_strategies": ["files", "folders", "auto"],
+      "wave_orchestration": {
+        "enabled": true,
+        "strategies": ["progressive", "systematic", "adaptive", "enterprise"],
+        "complexity_threshold": 0.4,
+        "min_wave_tasks": 3
+      },
+      "parallel_coordination": {
+        "max_parallel_agents": 7,
+        "synchronization_timeout_ms": 5000,
+        "resource_sharing_enabled": true
+      },
+      "learning_integration": {
+        "delegation_learning": true,
+        "wave_learning": true,
+        "cross_session_learning": true
+      }
+    }
+  }
+}
+```
+
+## MODE_Task_Management Integration
+
+The hook implements **MODE_Task_Management** through comprehensive integration with the task management framework:
+
+### Task Management Layer Integration
+
+```python
+# Load task management configuration
+self.task_config = config_loader.get_section('session', 'task_management', {})
+
+# Integration with task management layers
+# Layer 1: TodoRead/TodoWrite (Session Tasks) - Real-time state management
+# Layer 2: /task Command (Project Management) - Cross-session persistence
+# Layer 3: /spawn Command (Meta-Orchestration) - Complex multi-domain operations
+# Layer 4: /loop Command (Iterative Enhancement) - Progressive refinement workflows
+```
+
+**Framework Integration Points:**
+- **Session Task Tracking**: Integration with TodoWrite for task completion analytics
+- **Project Task Coordination**: Cross-session task management integration
+- **Meta-Orchestration**: Complex multi-domain operation coordination
+- **Iterative Enhancement**: Progressive refinement and quality improvement cycles
+
+### Auto-Activation Patterns
+
+The hook supports MODE_Task_Management auto-activation patterns:
+
+```python
+# Auto-activation triggers from MODE_Task_Management:
+# - Sub-Agent Delegation: >2 directories OR >3 files OR complexity >0.4
+# - Wave Mode: complexity ≥0.4 AND files >3 AND operation_types >2
+# - Loop Mode: polish, refine, enhance, improve keywords detected
+```
+
+**Detection Patterns:**
+- **Multi-Step Operations**: 3+ step sequences with dependency analysis
+- **Complexity Thresholds**: Operations exceeding 0.4 complexity score
+- **File Count Triggers**: 3+ files for delegation, 2+ directories for coordination
+- **Performance Opportunities**: Auto-detect parallelizable operations with time estimates
+
+## Coordination Effectiveness
+
+The hook provides comprehensive **success metrics for delegation** through multiple measurement dimensions:
+
+### Overall Effectiveness Calculation
+
+```python
+'performance_summary': {
+    'overall_effectiveness': (
+        task_analysis['completion_quality'] * 0.4 +
+        delegation_analysis['delegation_value'] * 0.3 +
+        coordination_analysis['synchronization_effectiveness'] * 0.3
+    ),
+    'delegation_success': delegation_analysis['delegation_value'] > 0.6,
+    'coordination_success': coordination_analysis['synchronization_effectiveness'] > 0.7,
+    'learning_value': wave_metrics.get('wave_learning_value', 0.5)
+}
+```
+
+**Effectiveness Dimensions:**
+- **Task Quality (40%)**: Output quality and completion success
+- **Delegation Value (30%)**: Effectiveness of delegation strategy and execution
+- **Coordination Success (30%)**: Synchronization and coordination effectiveness
+
+### Success Thresholds
+
+```python
+# Success criteria
+delegation_success = delegation_analysis['delegation_value'] > 0.6
+coordination_success = coordination_analysis['synchronization_effectiveness'] > 0.7
+wave_success = wave_metrics['wave_performance'] > 0.8
+```
+
+**Performance Benchmarks:**
+- **Delegation Success**: >60% delegation value threshold
+- **Coordination Success**: >70% synchronization effectiveness threshold  
+- **Wave Success**: >80% wave performance threshold
+- **Overall Effectiveness**: Composite score incorporating all dimensions
+
+### Optimization Recommendations
+
+```python
+def _generate_optimization_insights(self, context: dict, task_analysis: dict, 
+                                  delegation_analysis: dict, coordination_analysis: dict) -> dict:
+    """Generate optimization insights for future delegations."""
+    insights = {
+        'delegation_optimizations': [],
+        'coordination_improvements': [],
+        'wave_strategy_recommendations': [],
+        'performance_enhancements': [],
+        'learning_opportunities': []
+    }
+```
+
+**Recommendation Categories:**
+- **Delegation Optimizations**: Alternative strategies, overhead reduction, task partitioning improvements
+- **Coordination Improvements**: Synchronization mechanism optimization, data exchange pattern improvements
+- **Wave Strategy Recommendations**: Orchestration strategy adjustments, task distribution optimization
+- **Performance Enhancements**: Execution speed optimization, resource utilization improvements
+- **Learning Opportunities**: Pattern recognition, cross-agent learning, continuous improvement areas
+
+## Error Handling and Resilience
+
+The hook implements robust error handling with graceful degradation:
+
+```python
+def _create_fallback_report(self, subagent_data: dict, error: str) -> dict:
+    """Create fallback coordination report on error."""
+    return {
+        'subagent_id': subagent_data.get('subagent_id', 'unknown'),
+        'task_id': subagent_data.get('task_id', 'unknown'),
+        'completion_timestamp': time.time(),
+        'error': error,
+        'fallback_mode': True,
+        
+        'task_completion': {
+            'success': False,
+            'quality_score': 0.0,
+            'efficiency_score': 0.0,
+            'error_occurred': True
+        }
+    }
+```
+
+**Error Recovery Strategies:**
+- **Graceful Degradation**: Fallback coordination reports when analysis fails
+- **Context Preservation**: Maintain essential coordination data even during errors
+- **Error Logging**: Comprehensive error tracking for debugging and improvement
+- **Performance Monitoring**: Continue performance tracking even in error conditions
+
+## Integration with SuperClaude Framework
+
+The hook integrates seamlessly with the broader SuperClaude framework:
+
+### Framework Components
+
+- **Learning Engine Integration**: Records coordination patterns for continuous improvement
+- **Pattern Detection**: Identifies successful delegation and coordination patterns
+- **MCP Intelligence**: Coordinates with MCP servers for enhanced analysis
+- **Compression Engine**: Optimizes data storage and transfer for coordination analytics
+- **Framework Logic**: Implements SuperClaude operational principles and patterns
+
+### Quality Gates Integration
+
+The hook contributes to SuperClaude's 8-step quality validation cycle:
+- **Step 2.5**: Task management validation during orchestration operations
+- **Step 7.5**: Session completion verification and summary documentation
+- **Continuous**: Real-time metrics collection and performance monitoring
+- **Post-Session**: Comprehensive session analytics and completion reporting
+
+### Future Enhancements
+
+Planned improvements for enhanced delegation coordination:
+- **Predictive Delegation**: ML-based delegation strategy recommendation
+- **Cross-Project Learning**: Delegation pattern sharing across projects
+- **Real-Time Optimization**: Dynamic delegation adjustment during execution
+- **Advanced Wave Strategies**: More sophisticated wave orchestration patterns
+- **Resource Prediction**: Predictive resource allocation for delegated tasks